NDLI: Exploring pleiotropy using principal components

Content Provider	Springer Nature : BioMed Central
Author	Bensen, Jeannette T Lange, Leslie A Langefeld, Carl D Chang, Bao-Li Bleecker, Eugene R Meyers, Deborah A Xu, Jianfeng
Abstract	A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself.
Related Links	https://bmcgenomdata.biomedcentral.com/counter/pdf/10.1186/1471-2156-4-S1-S53.pdf
Ending Page	5
Page Count	5
Starting Page	1
File Format	HTM / HTML
ISSN	27306844
DOI	10.1186/1471-2156-4-S1-S53
Journal	BMC Genomic Data
Issue Number	1
Volume Number	4
Language	English
Publisher	BioMed Central
Publisher Date	2003-12-31
Access Restriction	Open
Subject Keyword	Life Sciences Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Genetics and Population Dynamics multivariate analysis principle components quantitative traits heritability estimates linkage
Content Type	Text
Resource Type	Article
Subject	Health Informatics Genetics
Journal Impact Factor	1.9/2023
5-Year Journal Impact Factor	1.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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